Accelerating Deep Learning Inference Using NVIDIA TensorRT

Artificial Intelligence and deep learning models are widely used in healthcare, autonomous systems, robotics, and natural language processing. Although these models achieve high accuracy during training, efficient deployment for inference remains a major challenge because of latency, computational overhead, and memory limitations. This paper examines NVIDIA TensorRT as a high performance inference optimization framework designed to accelerate AI model deployment on NVIDIA GPUs. TensorRT applies advanced optimization techniques including layer fusion, precision calibration, kernel auto tuning, and dynamic tensor memory management to improve execution efficiency. The study analyzes the architecture, workflow, optimization strategies, applications, and deployment considerations of TensorRT. The results indicate that TensorRT significantly reduces inference latency and improves throughput while maintaining model accuracy. The proposed analysis demonstrates that TensorRT is an essential framework for enabling efficient, scalable, and real time deployment of modern deep learning systems.